Table REF  summarizes the hyperparameters used for the experiment.
The top row identifies the categories of the other rows.
Specific parameters are shown for each model.
Unimportant parameters were set to their default values according to their respective research articles.
We employed the Adam optimizer with no weight decay.
The feature net of NAM-NC first passes through ExU and then through its linear combination layer [1]}.
According to NAM, 1,000 nodes of ExU are sufficient to extract the feature scalars from all inputs.
However, in our experiment, only 100 nodes of ExU were used.
Thus, we extracted the feature scalar from the TS points without using a linear combination multilayer structure.
Additionally, a rectified linear unit (ReLU) with a limited maximum value for NAM was used to lower the dependence of specific feature-nets while hindering training the time-series data.
Thus, we used a leaky ReLU for NAM-NC’s feature-net activation.
